161 lines
4.8 KiB
Markdown
161 lines
4.8 KiB
Markdown
# Haiku SQLite RAG
|
|
|
|
A SQLite-based Retrieval-Augmented Generation (RAG) system built for efficient document storage, chunking, and hybrid search capabilities.
|
|
|
|
## Features
|
|
|
|
- **Document Management**: Store and manage documents with automatic content parsing
|
|
- **Smart Updates**: Intelligent file/URL monitoring with MD5-based change detection
|
|
- **Hybrid Search**: Full-text search (FTS5) combined with vector embeddings
|
|
- **Multi-format Support**: Parse 40+ file formats including PDF, DOCX, HTML, Markdown, and more
|
|
- **Web Content**: Direct URL ingestion with automatic content type detection
|
|
- **Vector Embeddings**: Uses sqlite-vec for efficient similarity search
|
|
- **Automatic Chunking**: Intelligent document segmentation for better retrieval
|
|
|
|
## Installation
|
|
|
|
```bash
|
|
uv pip install haiku.rag
|
|
```
|
|
|
|
or for development, checkout the repository and then,
|
|
|
|
```bash
|
|
# Install dependencies
|
|
uv sync
|
|
|
|
# Activate virtual environment
|
|
source .venv/bin/activate
|
|
```
|
|
|
|
## Quick Start
|
|
|
|
```python
|
|
from pathlib import Path
|
|
from haiku.rag.client import HaikuRAG
|
|
|
|
# Use as async context manager (recommended)
|
|
async with HaikuRAG("path/to/database.db") as client:
|
|
# Create document from text
|
|
doc = await client.create_document(
|
|
content="Your document content here",
|
|
uri="doc://example",
|
|
metadata={"source": "manual", "topic": "example"}
|
|
)
|
|
|
|
# Create document from file (auto-parses content)
|
|
doc = await client.create_document_from_source("path/to/document.pdf")
|
|
|
|
# Create document from URL
|
|
doc = await client.create_document_from_source("https://example.com/article.html")
|
|
|
|
# Retrieve documents
|
|
doc = await client.get_document_by_id(1)
|
|
doc = await client.get_document_by_uri("file:///path/to/document.pdf")
|
|
|
|
# List all documents with pagination
|
|
docs = await client.list_documents(limit=10, offset=0)
|
|
|
|
# Update document content
|
|
doc.content = "Updated content"
|
|
await client.update_document(doc)
|
|
|
|
# Delete document
|
|
await client.delete_document(doc.id)
|
|
|
|
# Search documents using hybrid search (vector + full-text)
|
|
results = await client.search("machine learning algorithms", limit=5)
|
|
for chunk, score in results:
|
|
print(f"Score: {score:.3f}")
|
|
print(f"Content: {chunk.content}")
|
|
print(f"Document ID: {chunk.document_id}")
|
|
print("---")
|
|
|
|
|
|
# Or use without the context manager.
|
|
client = HaikuRAG(":memory:")
|
|
try:
|
|
# ... operations ...
|
|
finally:
|
|
client.close()
|
|
```
|
|
|
|
## Search Functionality
|
|
|
|
`haiku.rag` provides hybrid search combining vector similarity and full-text search:
|
|
1. **Vector Search**: Uses embeddings to find semantically similar content
|
|
2. **Full-text Search**: Uses SQLite FTS5 for exact keyword matching
|
|
3. **Hybrid Ranking**: Combines both using Reciprocal Rank Fusion (RRF)
|
|
4. **Chunked Results**: Returns relevant document chunks with scores
|
|
|
|
```python
|
|
async with HaikuRAG("database.db") as client:
|
|
# Basic search
|
|
results = await client.search("your query here")
|
|
|
|
# Search with custom parameters
|
|
results = await client.search(
|
|
query="machine learning",
|
|
limit=10, # Maximum results to return
|
|
k=60 # RRF parameter for reciprocal rank fusion
|
|
)
|
|
|
|
# Process results
|
|
for chunk, relevance_score in results:
|
|
print(f"Relevance: {relevance_score:.3f}")
|
|
print(f"Content: {chunk.content}")
|
|
print(f"From document: {chunk.document_id}")
|
|
```
|
|
|
|
## Smart Document Updates
|
|
|
|
The system automatically tracks file changes using MD5 hashes:
|
|
|
|
```python
|
|
async with HaikuRAG("database.db") as client:
|
|
# First call - creates new document
|
|
doc1 = await client.create_document_from_source("document.txt")
|
|
|
|
# Second call - no changes, returns existing document (no processing)
|
|
doc2 = await client.create_document_from_source("document.txt")
|
|
assert doc1.id == doc2.id
|
|
|
|
# After file modification - automatically updates existing document
|
|
# File content changed...
|
|
doc3 = await client.create_document_from_source("document.txt")
|
|
assert doc1.id == doc3.id # Same document
|
|
assert doc3.content != doc1.content # Updated content
|
|
```
|
|
|
|
## Supported File Formats
|
|
|
|
The system supports 40+ file formats through MarkItDown:
|
|
|
|
- **Documents**: PDF, DOCX, PPTX, XLSX
|
|
- **Web**: HTML, XML
|
|
- **Text**: TXT, MD, CSV, JSON, YAML
|
|
- **Code**: PY, JS, TS, C, CPP, JAVA, GO, RS, and more
|
|
- **Media**: MP3, WAV (transcription)
|
|
|
|
## Document Metadata
|
|
|
|
Documents automatically include metadata:
|
|
|
|
```python
|
|
doc = await client.create_document_from_source("example.pdf")
|
|
print(doc.metadata)
|
|
# {
|
|
# "contentType": "application/pdf",
|
|
# "md5": "abc123...",
|
|
# "custom_field": "value" # Your custom metadata
|
|
# }
|
|
```
|
|
|
|
## Contributing
|
|
|
|
1. Fork the repository
|
|
2. Create a feature branch
|
|
3. Add tests for new functionality
|
|
4. Ensure all tests pass: `pytest`
|
|
5. Run type checking & linting with `pyright` & `ruff check`
|
|
6. Submit a pull request
|